Complexity and Animal Models

Biological systems are extremely complex. This nugget of wisdom may seem trivial but it is a lesson the scientific and medical communities have been learning over and over again for a couple of centuries. Every time we think we understand a biological system we find there is a deeper level of complexity, or another layer of interactions we had not previously taken into consideration.

Although I am almost certain that Dr Novella does not share our views on animal models, his statement is one we strongly agree with and expand upon in Animal Models in Light of Evolution.

Complexity is somewhat difficult to explain in a short space, but I will give it a try.

Remember the computer experiments conducted by Edward Lorenz in the early 1960s that were the beginning of chaos theory? Lorenz was simulating weather conditions on the computer. He rounded off a variable from six decimal points to three and ran his computer program again only to find that he obtained completely different results. What Lorenz discovered was that very small changes in initial conditions produced large changes in the long-term outcome. That is part of chaos theory and complexity as a scientific discipline is related to chaos theory.

Complexity as a science relates to the structure and order that we find between the conditions of total randomness or chaos and the conditions that lead to total order. Living systems, like animals, are examples of complex systems. One important thing about complex systems is that the causes and effects of the events that a complex system experiences are not proportional to each other. Another important characteristic is that the different parts of a complex system are linked to and affect one another in a synergistic manner. In other words, there is positive and negative feedback in a complex system.

Probably the most important aspects of complex system as they pertain to our position are: 1) that complex systems are very dependent upon initial conditions; 2) that perturbations to the system have effects that are nonlinear in other words large perturbations may result in no change while small perturbations may cause havoc; and 3) that the whole is greater than the sum of the parts. To put all this in the context of using animals in research, very small differences in the genetic makeup and or organization of two otherwise very similar species can result in very different responses to drugs and disease.

Complexity is in some ways opposite to the concept of reductionism. Ernst Mayr defines reductionism as: “The belief that the higher levels of integration of a complex system can be fully explained through a knowledge of the smallest components.” [(Mayr 2002) p290]

Mayr also pointed out that complexity has been around since Alex Novikoff described it in 1947.

Alex Novikoff (1947), however, spelled out in considerable detail why an explanation of living organisms has to be holistic. "What are wholes on one level become parts on a higher one . . . both parts and holes are material entities, and integration results from the interaction of parts as a consequence of their properties." Holism, since it rejects reduction, "does not regard living organisms as machines made of a multitude of discrete parts (physico-chemical units), removable like pistons of an engine and capable of description without regard to the system from which they are removed." Owing to the interaction of the parts, a description of the isolated parts fails to convey the properties of the system as a whole. It is the organization of these parts that controls the entire system. [(Mayr 1998) p18]

On a similar note, the biologist AG Cairns-Smith pointed out:

Subsystems are highly interlocked…[P]rotein are needed to make catalysts, yet catalysts are needed to make proteins. Nucleic acids are needed to make proteins, yet proteins are needed to make nucleic acids. Proteins and lipids are needed to make membranes, yet membranes are needed to provide protection for all the chemical processes going on in a cell. The whole is presupposed by all the parts. The inter-locking is tight and critical. At the centre everything depends on everything. [(Cairns-Smith 1986) p39]

The reductionist method of dissecting biological systems into their constituent parts has been effective in explaining the chemical basis of numerous living processes. However, many biologists now realize that this approach has reached its limit. Biological systems are extremely complex and have emergent properties that cannot be explained, or even predicted, by studying their individual parts. The reductionist approach—although successful in the early days of molecular biology— underestimates this complexity and therefore has an increasingly detrimental influence on many areas of biomedical research, including drug discovery and vaccine development . . .

Today, it is clear that the specificity of a complex biological activity does not arise from the specificity of the individual molecules that are involved, as these components frequently function in many different processes. For instance, genes that affect memory formation in the fruit fly encode proteins in the cyclic AMP (cAMP) signalling pathway that are not specific to memory. It is the particular cellular compartment and environment in which a second messenger, such as cAMP, is released that allow a gene product to have a unique effect. Biological specificity results from the way in which these components assemble and function together (Morange, 2001a). Interactions between the parts, as well as influences from the environment, give rise to new features, such as network behaviour (Alm & Arkin, 2003), which are absent in the isolated components . . .

Because complex systems have emergent properties, it should be clear from the preceding discussion that their behaviour cannot be understood or predicted simply by analysing the structure of their components. The constituents of a complex system interact in many ways, including negative feedback and feed-forward control, which lead to dynamic features that cannot be predicted satisfactorily by linear mathematical models that disregard cooperativity and non-additive effects. In view of the complexity of informational pathways and networks, new types of mathematics are required for modelling these systems (Aderem & Smith, 2004).

Another essential property of complex biological systems is their robustness (Csete & Doyle, 2002; Kitano, 2002). Robust systems tend to be impervious to changes in the environment because they are able to adapt and have redundant components that can act as a backup if individual components fail. A further characteristic of complex systems is their modularity (Alm & Arkin, 2003): subsystems are physically and functionally insulated so that failure in one module does not spread to other parts with possibly lethal consequences. This modularity, however, does not prevent different compartments from communicating with each other (Weng et al, 1999). An additional peculiarity of complex biological systems is that they are open—that is, they exchange matter and energy with their environment—and are therefore not in thermodynamic equilibrium. In the past, the reductionist agenda of molecular biologists has made them turn a blind eye to emergence, complexity and robustness, which has had a profound influence on biological and biomedical research during the past 50 years. In the following sections, I describe some of the harmful effects of reductionist thinking in drug-discovery programmes and vaccinology . . .

Another defect of reductionist thinking is that it analyses complex network interactions in terms of simple causal chains and mechanistic models. This overlooks the fact that any clinical state is the end result of many biochemical pathways and networks, and fails to appreciate that diseases result from alterations to complex systems of homeostasis. Reductionists favour causal explanations that give undue explanatory weight to a single factor. By contrast, many biologists favour functional explanations for a structure or cellular process, and emphasize the selective advantage of these features during evolutionary history—after all, evolution selects for function, not structure. Functional explanations are more useful for understanding complex biological systems with many interactions than are causal explanations that give unwarranted importance to a single factor (Van Regenmortel, 2002). Lewontin (2000) also stressed the reciprocal relationships between genes, organisms and their environment, in which all three elements act as both causes and effects. (Van Regenmortel 2004) (The entire article can be accessed via the above link.)

In the final analysis, the fact that animals and humans are complex living systems means that animals will not be able to predict human response to drugs and disease.

Of ten medications withdrawn from the US market between 1998 and 2001, eight were withdrawn because they induced certain severe side effects more frequently in women than in men (General Accounting Office 2001). Men and women are obviously similar in terms of evolutionary biology and gene regulation, but they responded very differently to these drugs. Studies have revealed that one strain of mice could have a gene removed, while another strain would die without the gene (Nijhout 2003; Pearson 2002). Iressa or gefitinib was thought to be ineffective as an anticancer drug but further analysis revealed it to be very effective for people with a specific genetic mutation. Today, because of pharmacogenetics, and other advances, we are on the verge of Personalized Medicine—drugs and treatments tailor made for the individual.

This again illustrates how very small differences between complex systems can result in profound differences in disease and drug response. If men do not respond the same as women, and one strain of mice does not respond the same as another why are we testing animals in order to predict human response to drugs and disease?

Evolution, complexity theory and genetics explain why animal testing should not be an effective means of discovering what a drug will do in humans and how human will respond to diseases and most importantly empirical data supports this. Furthermore, the level of examination has changed since the 19th century when animals were primarily used to find commonalities across species lines. As our examination of living systems has become increasingly fine-grained, we have found that subtle differences between organisms tend to outweigh gross similarities. Science could and did use animals to shed light on shared functions such as the basic function of the liver and pancreas, but today we are studying drug response and disease at the level that defines not only a species, but in many cases the individual.

The intact systems argument has historically been the animal modelers’ main argument: “We must test on animals because no experimental system be it in vitro, in silico, mathematical modeling, and so forth can predict what a drug will do to the intact living human system.” Ironically, it is the fact that each intact living system is a differently complex system with unique evolutionary trajectories that invalidates the use of animal models. Complex systems are more than the sum of their parts, and complex systems that are almost identical still respond differently to the same drug or disease. The implicit claim in the intact systems argument, that humans and other animal species are the same biochemical animals just dressed up differently, is simply not true.